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Engineering, 2020 Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop explainable artificial intelligence (XAI) algorithms ...
Kun Kuang +9 more
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Recursive Causal Inference Algorithm Based on Partial Correlation Test [PDF]
Jisuanji gongcheng, 2022 Causal inference is an important tool for mining relationships between observed data points.The causal inference algorithm encounters the problems of redundant tests and low test efficiency in high-dimensional cases, which limits the application of ...
CHEN Mingjie, ZHANG Hao, PENG Yuzhong, XIE Feng, PANG Yue
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International Journal of Public Health, 2023 Objectives: The aging of the South African population could have profound implications for the independence and overall quality of life of older adults as life expectancy increases. While there is evidence that lifetime socio-economic status shapes risks
Keletso Makofane +4 more
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Causal inference: relating language to event representations and events in the world
Frontiers in Psychology, 2023 Events are not isolated but rather linked to one another in various dimensions. In language processing, various sources of information—including real-world knowledge, (representations of) current linguistic input and non-linguistic visual context—help ...
Yipu Wei +3 more
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Introducing Causal Inference Using Bayesian Networks and do-Calculus
Journal of Statistics and Data Science Education, 2022 We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced ...
Yonggang Lu, Qiujie Zheng, Daniel Quinn
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Matching methods for causal inference: A review and a look forward. [PDF]
Statistical Science, 2010 When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing
E. Stuart
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Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond [PDF]
Transactions of the Association for Computational Linguistics, 2021 A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally ...
Amir Feder +12 more
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A First Course in Causal Inference [PDF]
, 2023 I developed the lecture notes based on my ``Causal Inference'' course at the University of California Berkeley over the past seven years. Since half of the students were undergraduates, my lecture notes only required basic knowledge of probability theory,
Peng Ding
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On the dimensional indeterminacy of one-wave factor analysis under causal effects
Journal of Causal Inference, 2023 It is shown, with two sets of indicators that separately load on two distinct factors, independent of one another conditional on the past, that if it is the case that at least one of the factors causally affects the other, then, in many settings, the ...
VanderWeele Tyler J. +1 more
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Causal Inference in Recommender Systems: A Survey and Future Directions [PDF]
ACM Trans. Inf. Syst., 2022 Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature ...
Chen Gao +5 more
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